Pop nat analysis
Laure Olazcuaga, Nicolas Rode
04-December-2021
1 DATA
#############################
#PERFORMANCE
#############################
data_PERF <- import_data(dataset = "DATACOMPLET_PERF.csv",
trait = "performance",
remove_testenvt = c("Grape","GF"),
remove_pop = c("WT3"),
remove_rate = NA)## [1] "Data (541 and 602 tubes for the first and third generation respectively) where i) the number of eggs was NA (5 and 0 tubes for the first and third generation respectively); or ii) the number of adults was NA (0 and 0 tubes for the first and third generation respectively); or iii) the number of eggs was zero -Emergence rate = NaN- (99 and 0 tubes for the first and third generation respectively); or iv) the number of adults was higher than the initial number of eggs (50 and 1 tubes for the first and third generation respectively) were not removed."head(data_PERF)## Generation Experiment Original_environment Population Date_P Date_C_O
## 8 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 13 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 14 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 16 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 20 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 24 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## Date_C_A Row Column Rack Test_environment Nb_eggs Obs_O Nb_adults Obs_A
## 8 19/10/2018 L1 C3 1 Cherry 3 LO 2 LO
## 13 19/10/2018 L1 C8 1 Blackberry 1 LO 2 LO
## 14 19/10/2018 L1 C9 1 Cherry 2 LO 1 LO
## 16 19/10/2018 L2 C1 1 Blackberry 1 LO 0 LO
## 20 19/10/2018 L2 C5 1 Strawberry 2 LO 2 LO
## 24 19/10/2018 L2 C9 1 Cherry 1 LO 1 LO
## EggScore EggScoreFive EggScoreSmall SA IndicG0 IndicG2 SAIndicG0
## 8 1 1 1 0 1 0 0
## 13 1 1 1 1 1 0 1
## 14 1 1 1 0 1 0 0
## 16 1 1 1 1 1 0 1
## 20 1 1 1 0 1 0 0
## 24 1 1 1 0 1 0 0
## fruit_hab fruit_hab_ng fruit_gen hab_gen
## 8 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## 13 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0 Blackberry_G0
## 14 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## 16 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0 Blackberry_G0
## 20 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0 Strawberry_G0
## 24 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## pop_gen Rate
## 8 Blackberry45_G0 0.6666667
## 13 Blackberry45_G0 2.0000000
## 14 Blackberry45_G0 0.5000000
## 16 Blackberry45_G0 0.0000000
## 20 Blackberry45_G0 1.0000000
## 24 Blackberry45_G0 1.0000000tapply(data_PERF$Nb_adults,list(data_PERF$Original_environment,data_PERF$Generation),length)## G0 G2
## Blackberry 169 320
## Cherry 233 143
## Strawberry 35 139#############################
#EMERGENCE RATE
#############################
data_PERF_Rate <- import_data(dataset = "DATACOMPLET_PERF.csv",
trait = "performance",
remove_testenvt = c("Grape","GF"),
remove_pop = c("WT3"),
remove_rate = TRUE)## [1] "Data (541 and 602 tubes for the first and third generation respectively) where i) the number of eggs was NA (5 and 0 tubes for the first and third generation respectively); or ii) the number of adults was NA (0 and 0 tubes for the first and third generation respectively); or iii) the number of eggs was zero -Emergence rate = NaN- (99 and 0 tubes for the first and third generation respectively); or iv) the number of adults was higher than the number of eggs (50 and 1 tubes for the first and third generation respectively) were removed."head(data_PERF_Rate)## Generation Experiment Original_environment Population Date_P Date_C_O
## 8 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 14 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 16 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 20 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 24 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## 33 G0 Plasticity Blackberry Blackberry45 3/10/2018 4/10/18
## Date_C_A Row Column Rack Test_environment Nb_eggs Obs_O Nb_adults Obs_A
## 8 19/10/2018 L1 C3 1 Cherry 3 LO 2 LO
## 14 19/10/2018 L1 C9 1 Cherry 2 LO 1 LO
## 16 19/10/2018 L2 C1 1 Blackberry 1 LO 0 LO
## 20 19/10/2018 L2 C5 1 Strawberry 2 LO 2 LO
## 24 19/10/2018 L2 C9 1 Cherry 1 LO 1 LO
## 33 19/10/2018 L3 C8 1 Blackberry 2 LO 2 LO
## EggScore EggScoreFive EggScoreSmall SA IndicG0 IndicG2 SAIndicG0
## 8 1 1 1 0 1 0 0
## 14 1 1 1 0 1 0 0
## 16 1 1 1 1 1 0 1
## 20 1 1 1 0 1 0 0
## 24 1 1 1 0 1 0 0
## 33 1 1 1 1 1 0 1
## fruit_hab fruit_hab_ng fruit_gen hab_gen
## 8 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## 14 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## 16 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0 Blackberry_G0
## 20 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0 Strawberry_G0
## 24 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0 Cherry_G0
## 33 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0 Blackberry_G0
## pop_gen Rate
## 8 Blackberry45_G0 0.6666667
## 14 Blackberry45_G0 0.5000000
## 16 Blackberry45_G0 0.0000000
## 20 Blackberry45_G0 1.0000000
## 24 Blackberry45_G0 1.0000000
## 33 Blackberry45_G0 1.0000000tapply(data_PERF_Rate$Nb_adults,list(data_PERF_Rate$Original_environment,
data_PERF_Rate$Generation),length)## G0 G2
## Blackberry 129 319
## Cherry 224 143
## Strawberry 34 139tapply(data_PERF_Rate$Rate,data_PERF_Rate$Generation,mean)## G0 G2
## 0.3593365 0.1857181## To test Rate higher than 1
# data_PERF_Rate <- import_data(dataset = "DATACOMPLET_PERF.csv",
# trait = "performance",
# remove_testenvt = c("Grape","GF"),
# remove_pop = c("WT3"),
# remove_rate = NA)
# #Replace Rate>1 by 1
# data_PERF_Rate$Rate[data_PERF_Rate$Nb_adults>=data_PERF_Rate$Nb_eggs] <- 1
###########################
#PREFERENCE
###########################
data_PREF <- import_data(dataset = "DATACOMPLET_PREF.csv",
trait = "preference",
remove_testenvt = NA,
remove_pop = c("WT3"),
remove_rate = NA)
head(data_PREF)## Generation Experiment BoxID Date_P Original_environment Population Line
## 1 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 2 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 3 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 4 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 5 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 2
## 6 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 2
## Column Test_environment Nb_eggs Date_C_O Obs_O SA IndicG0 IndicG2 SAIndicG0
## 1 1 Cranberry 0 13/12/2018 CD 0 1 0 0
## 2 2 Fig 0 13/12/2018 CD 0 1 0 0
## 3 3 Raspberry 0 13/12/2018 CD 0 1 0 0
## 4 4 Rosehips 0 13/12/2018 CD 0 1 0 0
## 5 1 Kiwi 0 13/12/2018 CD 0 1 0 0
## 6 2 Strawberry 1 13/12/2018 CD 0 1 0 0
## fruit_hab fruit_hab_ng fruit_gen hab_gen
## 1 Blackberry_Cranberry Blackberry_Cranberry_G0 Blackberry_G0 Cranberry_G0
## 2 Blackberry_Fig Blackberry_Fig_G0 Blackberry_G0 Fig_G0
## 3 Blackberry_Raspberry Blackberry_Raspberry_G0 Blackberry_G0 Raspberry_G0
## 4 Blackberry_Rosehips Blackberry_Rosehips_G0 Blackberry_G0 Rosehips_G0
## 5 Blackberry_Kiwi Blackberry_Kiwi_G0 Blackberry_G0 Kiwi_G0
## 6 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0 Strawberry_G0
## pop_gen
## 1 Blackberry33_G0
## 2 Blackberry33_G0
## 3 Blackberry33_G0
## 4 Blackberry33_G0
## 5 Blackberry33_G0
## 6 Blackberry33_G0tapply(data_PREF$Nb_eggs,list(data_PREF$Original_environment,data_PREF$Generation),length)## G0 G2
## Blackberry 696 1176
## Cherry 1200 624
## Strawberry 252 480###########################
#PREFERENCE 3 fruits
###########################
levels_test<-levels(data_PREF$Test_environment)
levels_original<-levels(data_PREF$Original_environment)
data_PREF_three <- import_data(dataset = "DATACOMPLET_PREF.csv",
trait = "preference",
remove_testenvt = usefun::outersect(levels_test,
levels_original),
remove_pop = c("WT3"),
remove_rate = NA)
head(data_PREF_three)## Generation Experiment BoxID Date_P Original_environment Population
## 6 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33
## 9 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33
## 11 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33
## 15 G0 Plasticity 884 19/09/2018 Blackberry Blackberry33
## 16 G0 Plasticity 884 19/09/2018 Blackberry Blackberry33
## 20 G0 Plasticity 884 19/09/2018 Blackberry Blackberry33
## Line Column Test_environment Nb_eggs Date_C_O Obs_O SA IndicG0 IndicG2
## 6 2 2 Strawberry 1 13/12/2018 CD 0 1 0
## 9 3 1 Blackberry 0 13/12/2018 CD 1 1 0
## 11 3 3 Cherry 0 13/12/2018 CD 0 1 0
## 15 1 3 Cherry 0 13/12/2018 CD 0 1 0
## 16 1 4 Strawberry 0 13/12/2018 CD 0 1 0
## 20 2 4 Blackberry 0 13/12/2018 CD 1 1 0
## SAIndicG0 fruit_hab fruit_hab_ng fruit_gen
## 6 0 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0
## 9 1 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0
## 11 0 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0
## 15 0 Blackberry_Cherry Blackberry_Cherry_G0 Blackberry_G0
## 16 0 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0
## 20 1 Blackberry_Blackberry Blackberry_Blackberry_G0 Blackberry_G0
## hab_gen pop_gen
## 6 Strawberry_G0 Blackberry33_G0
## 9 Blackberry_G0 Blackberry33_G0
## 11 Cherry_G0 Blackberry33_G0
## 15 Cherry_G0 Blackberry33_G0
## 16 Strawberry_G0 Blackberry33_G0
## 20 Blackberry_G0 Blackberry33_G0tapply(data_PREF_three$Nb_eggs,list(data_PREF_three$Original_environment,
data_PREF_three$Generation),length)## G0 G2
## Blackberry 174 294
## Cherry 300 156
## Strawberry 63 120resume_design<-tapply(as.factor(data_PREF_three$BoxID),list(data_PREF_three$Population,
data_PREF_three$Generation),length)
mean(resume_design[,1], na.rm = TRUE)/3## [1] 7.782609mean(resume_design[,2], na.rm = TRUE)/3## [1] 7.916667resume_design<-tapply(data_PERF$Nb_eggs,list(data_PERF$Population,
data_PERF$Generation),length)
mean(resume_design[,1], na.rm = TRUE)/3## [1] 6.069444mean(resume_design[,2], na.rm = TRUE)/3## [1] 8.026667dim(data_PREF_three)## [1] 1107 21dim(data_PREF)## [1] 4428 21head(data_PREF)## Generation Experiment BoxID Date_P Original_environment Population Line
## 1 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 2 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 3 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 4 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 1
## 5 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 2
## 6 G0 Plasticity 860 19/09/2018 Blackberry Blackberry33 2
## Column Test_environment Nb_eggs Date_C_O Obs_O SA IndicG0 IndicG2 SAIndicG0
## 1 1 Cranberry 0 13/12/2018 CD 0 1 0 0
## 2 2 Fig 0 13/12/2018 CD 0 1 0 0
## 3 3 Raspberry 0 13/12/2018 CD 0 1 0 0
## 4 4 Rosehips 0 13/12/2018 CD 0 1 0 0
## 5 1 Kiwi 0 13/12/2018 CD 0 1 0 0
## 6 2 Strawberry 1 13/12/2018 CD 0 1 0 0
## fruit_hab fruit_hab_ng fruit_gen hab_gen
## 1 Blackberry_Cranberry Blackberry_Cranberry_G0 Blackberry_G0 Cranberry_G0
## 2 Blackberry_Fig Blackberry_Fig_G0 Blackberry_G0 Fig_G0
## 3 Blackberry_Raspberry Blackberry_Raspberry_G0 Blackberry_G0 Raspberry_G0
## 4 Blackberry_Rosehips Blackberry_Rosehips_G0 Blackberry_G0 Rosehips_G0
## 5 Blackberry_Kiwi Blackberry_Kiwi_G0 Blackberry_G0 Kiwi_G0
## 6 Blackberry_Strawberry Blackberry_Strawberry_G0 Blackberry_G0 Strawberry_G0
## pop_gen
## 1 Blackberry33_G0
## 2 Blackberry33_G0
## 3 Blackberry33_G0
## 4 Blackberry33_G0
## 5 Blackberry33_G0
## 6 Blackberry33_G0levels(data_PREF$Population)## [1] "Blackberry31" "Blackberry32" "Blackberry33" "Blackberry34" "Blackberry35"
## [6] "Blackberry36" "Blackberry37" "Blackberry38" "Blackberry39" "Blackberry40"
## [11] "Blackberry43" "Blackberry44" "Blackberry45" "Cherry103" "Cherry104"
## [16] "Cherry3" "Cherry47" "Cherry50" "Cherry51" "Cherry52"
## [21] "Cherry6" "Cherry7" "Strawberry42" "Strawberry44" "Strawberry53"2 OVIPOSITION STIMULATION
2.1 Exploration data
tapply(data_PERF$Nb_eggs, list(data_PERF$Original_environment, data_PERF$Test_environment, data_PERF$Generation), mean, na.rm = TRUE)## , , G0
##
## Blackberry Cherry Strawberry
## Blackberry 6.52459 10.22034 7.306122
## Cherry 23.35443 19.77500 12.283784
## Strawberry 21.30769 33.60000 16.833333
##
## , , G2
##
## Blackberry Cherry Strawberry
## Blackberry 103.9720 121.8491 98.71963
## Cherry 131.7174 133.8400 103.51064
## Strawberry 119.1739 119.4894 111.56522ggplot2::ggplot(data = data_PERF[data_PERF$Generation=="G0",],
aes(x = Test_environment, y = Nb_eggs, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PERF,
aes(x = Nb_eggs, fill = Original_environment)) +
facet_wrap( ~ Generation) +
geom_histogram(position="identity", alpha=0.5) +
theme_LO_sober
2.2 Analysis
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment,
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PERF)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[4,2])/(1/anova(m1)[4, 1]))## [1] 1.971666(pvalue <- 1 - pf(Fratio, 1, anova(m1)[4, 1])) ## [1] 0.2548912#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0,
data = data_PERF)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[5,2])/(1/anova(m2)[5, 1]))## [1] 2.264412(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[5, 1]) )## [1] 0.2294357## F test for SA
(Fratio_NonGen <- (anova(m2)[4,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 1.369663(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[6, 1]) )## [1] 0.3263818## Compute R2 for SA
## Compute R2 = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(rsqgen <- 1-anova(m2)[5, 3]/((anova(m2)[3, 2]+anova(m2)[5, 2])/(anova(m2)[3, 1]+anova(m2)[5, 1])))## [1] 0.240181(rsqng <- 1-anova(m2)[6, 3]/((anova(m2)[4, 2]+anova(m2)[6, 2])/(anova(m2)[4, 1]+anova(m2)[6, 1])))## [1] 0.08459751##Plot
(PLOT_eggs_G0 <- plot_RTP_residuals(dataset = data_PERF, trait = "Nb_eggs", gen = "G0"))
(PLOT_eggs_G2 <- plot_RTP_residuals(dataset = data_PERF, trait = "Nb_eggs", gen = "G2"))
(PLOT_GEN_eggs_G0 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF,
trait = "Nb_eggs",
effect = "Non-genetic"))
(PLOT_GEN_eggs_G2 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF,
trait = "Nb_eggs",
effect = "Genetic"))
###PLOT
(PAIR_BLACK_CHERRY_G2_EGGS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_eggs", gen = "G2",
fruit1 = "Cherry", fruit2 = "Blackberry"))
(PAIR_CHERRY_STRAW_G2_EGGS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_eggs", gen = "G2",
fruit1 = "Strawberry", fruit2 = "Cherry"))
(PAIR_STRW_BLACK_G2_EGGS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_eggs", gen = "G2",
fruit1 = "Blackberry", fruit2 = "Strawberry"))
###PLOT
(PAIR_BLACK_CHERRY_EGGS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_eggs", gen = "Both",
fruit1 = "Cherry", fruit2 = "Blackberry"))
(PAIR_CHERRY_STRAW_EGGS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_eggs", gen = "Both",
fruit1 = "Strawberry", fruit2 = "Cherry"))
(PAIR_STRW_BLACK_EGGS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_eggs", gen = "Both",
fruit1 = "Blackberry",
fruit2 = "Strawberry"))
3 NUMBER OF ADULTS
3.1 Exploration data
tapply(data_PERF$Nb_adults, list(data_PERF$Original_environment, data_PERF$Test_environment, data_PERF$Generation), mean, na.rm = TRUE)## , , G0
##
## Blackberry Cherry Strawberry
## Blackberry 4.508197 4.457627 3.816327
## Cherry 6.873418 5.675000 2.378378
## Strawberry 12.230769 16.000000 5.416667
##
## , , G2
##
## Blackberry Cherry Strawberry
## Blackberry 27.31776 23.18868 13.15888
## Cherry 22.91304 23.32000 11.53191
## Strawberry 25.65217 22.36170 16.04348ggplot2::ggplot(data = data_PERF[data_PERF$Generation=="G0",],
aes(x = Test_environment, y = Nb_adults, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PERF[data_PERF$Generation=="G2",],
aes(x = Test_environment, y = Nb_adults, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PERF,
aes(x = Nb_adults, fill = Original_environment)) +
facet_wrap( ~ Generation) +
geom_histogram(position="identity", alpha=0.5) +
theme_LO_sober
3.2 Analysis
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment,
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PERF)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[4,2])/(1/anova(m1)[4, 1]))## [1] 2.01345(pvalue <- 1 - pf(Fratio, 1, anova(m1)[4, 1])) ## [1] 0.2509626#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0,
data = data_PERF)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[5,2])/(1/anova(m2)[5, 1]))## [1] 2.447544(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[5, 1]) )## [1] 0.2156678## F test for SA
(Fratio_NonGen <- (anova(m2)[4,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 1.111569(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[6, 1]) )## [1] 0.3691494## Compute R2 for SA
## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(r2_SA_genet <- 1-(anova(m2)[5, 3]/((anova(m2)[3, 2]+anova(m2)[5, 2])/(anova(m2)[3, 1]+anova(m2)[5, 1]))))## [1] 0.2657241(r2_SA_nongenet <- 1-(anova(m2)[6, 3]/((anova(m2)[4, 2]+anova(m2)[6, 2])/(anova(m2)[4, 1]+anova(m2)[6, 1]))))## [1] 0.027135413.3 Analysis with eggs as covariable
#######################################################
## Should we consider the number of eggs? ###
#######################################################
# Original
m1 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0,
data = data_PERF)
## Correction for number of eggs
m2 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
log(Nb_eggs+1),
data = data_PERF)
## With egg score
m3 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScore,
data = data_PERF)
## Compare with 5 egg scores
m4 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScoreFive,
data = data_PERF)
## Compare with EggScoreSmall
m5 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScoreSmall,
data = data_PERF)
MuMIn::model.sel(m1, m2, m3, m4, m5)## Model selection table
## (Int) hab_gen pop_gen SA IG0:SA Org_env:Tst_env IG0:Org_env:Tst_env
## m2 -0.06648 + + + + + +
## m5 0.41840 + + + + + +
## m3 0.42840 + + + + + +
## m4 0.44140 + + + + + +
## m1 0.62560 + + + + + +
## log(Nb_egg+1) EgS ESF ESS family df logLik AICc delta
## m2 0.5125 gaussian(identity) 63 -977.876 2090.0 0.00
## m5 + gaussian(identity) 67 -1040.074 2223.5 133.51
## m3 + gaussian(identity) 65 -1045.327 2229.5 139.45
## m4 + gaussian(identity) 66 -1044.861 2230.8 140.80
## m1 gaussian(identity) 62 -1118.184 2368.4 278.35
## weight
## m2 1
## m5 0
## m3 0
## m4 0
## m1 0
## Models ranked by AICc(x)# Models are not all fitted to the same data: because 6 tubes without Nb_eggs are missing for m2, m3, m4 and m5
## Cl= The best model is when the number of eggs is considered as a continuous variable
### model m2 provides a better description of the data than model m1
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment + log(Nb_eggs+1),
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PERF)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[5,2])/(1/anova(m1)[5, 1]))## [1] 12.95037(pvalue <- 1 - pf(Fratio, 1, anova(m1)[5, 1])) ## [1] 0.03679697#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(log(Nb_adults+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
log(Nb_eggs+1),
data = data_PERF)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 17.09331(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[6, 1]) )## [1] 0.02567856## F test for SA
(Fratio_NonGen <- (anova(m2)[5,2]/anova(m2)[7,2])/(1/anova(m2)[7, 1]))## [1] 0.6257893(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[7, 1]) )## [1] 0.4866763## Compute R2 for SA
## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(r2_SA_genet <- 1-(anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))))## [1] 0.8009287(r2_SA_nongenet <- 1-(anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))))## [1] -0.1032081##Plot
(PLOT_adult_G0 <- plot_RTP_residuals(dataset = data_PERF, trait = "Nb_adults", gen = "G0"))
(PLOT_adult_G2 <- plot_RTP_residuals(dataset = data_PERF, trait = "Nb_adults", gen = "G2"))
(PLOT_GEN_adult_G0 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF,
trait = "Nb_adults",
effect = "Non-genetic"))
(PLOT_GEN_adult_G2 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF,
trait = "Nb_adults",
effect = "Genetic"))
###PLOT
(PAIR_BLACK_CHERRY_G2_ADULTS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_adults", gen = "G2",
fruit1 = "Cherry", fruit2 = "Blackberry"))
(PAIR_CHERRY_STRAW_G2_ADULTS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_adults", gen = "G2",
fruit1 = "Strawberry", fruit2 = "Cherry"))
(PAIR_STRW_BLACK_G2_ADULTS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_adults", gen = "G2",
fruit1 = "Blackberry", fruit2 = "Strawberry"))
###PLOT
(PAIR_BLACK_CHERRY_ADULTS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_adults",
gen = "Both",
fruit1 = "Cherry",
fruit2 = "Blackberry"))
(PAIR_CHERRY_STRAW_ADULTS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_adults",
gen = "Both",
fruit1 = "Strawberry",
fruit2 = "Cherry"))
(PAIR_STRW_BLACK_ADULTS <- plot_PairwisePOP_residuals(dataset = data_PERF,
trait = "Nb_adults",
gen = "Both",
fruit1 = "Blackberry",
fruit2 = "Strawberry"))
4 EGG-TO-ADULT SURVIVAL
4.1 Exploration data
tapply(data_PERF_Rate$Rate, list(data_PERF_Rate$Original_environment,
data_PERF_Rate$Test_environment,
data_PERF_Rate$Generation), mean, na.rm = TRUE)## , , G0
##
## Blackberry Cherry Strawberry
## Blackberry 0.6125868 0.4703813 0.5248347
## Cherry 0.2420863 0.3199467 0.2176492
## Strawberry 0.2888558 0.3595403 0.3373855
##
## , , G2
##
## Blackberry Cherry Strawberry
## Blackberry 0.2579090 0.1923140 0.1345134
## Cherry 0.1855892 0.1801664 0.1103531
## Strawberry 0.2206513 0.1950778 0.1619433ggplot2::ggplot(data = data_PERF_Rate[data_PERF_Rate$Generation=="G0",],
aes(x = Test_environment, y = Rate, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PERF_Rate[data_PERF_Rate$Generation=="G2",],
aes(x = Test_environment, y = Rate, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PERF_Rate,
aes(x = Rate, fill = Original_environment)) +
facet_wrap( ~ Generation) +
geom_histogram(position="identity", alpha=0.5) +
theme_LO_sober
lattice::xyplot(Rate~Nb_eggs|Original_environment*Test_environment,
data=data_PERF_Rate)
lattice::xyplot(Rate~EggScore|Original_environment*Test_environment,
data=data_PERF_Rate)
lattice::bwplot(Rate~EggScore|Original_environment*Test_environment,
data=data_PERF_Rate)
lattice::bwplot(Rate~EggScoreFive|Original_environment*Test_environment,
data=data_PERF_Rate)
lattice::bwplot(Rate~EggScoreSmall|Original_environment*Test_environment,
data=data_PERF_Rate)
AvEmergenceRate <- tapply(data_PERF_Rate$Rate,
list(data_PERF_Rate$EggScoreFive,
data_PERF_Rate$Original_environment,
data_PERF_Rate$Test_environment),mean)
tapply(data_PERF_Rate$Rate, list(data_PERF_Rate$EggScoreFive,
data_PERF_Rate$Original_environment,
data_PERF_Rate$Test_environment), length)## , , Blackberry
##
## Blackberry Cherry Strawberry
## 1 58 69 13
## 2 42 22 15
## 3 41 17 20
## 4 10 10 8
## 5 3 4 2
##
## , , Cherry
##
## Blackberry Cherry Strawberry
## 1 45 70 8
## 2 31 18 19
## 3 50 21 21
## 4 23 11 9
## 5 2 7 NA
##
## , , Strawberry
##
## Blackberry Cherry Strawberry
## 1 42 72 18
## 2 55 24 13
## 3 39 16 16
## 4 7 6 9
## 5 NA NA 2AvEmergenceRate[, , "Blackberry"][, "Cherry"]/AvEmergenceRate[, , "Blackberry"][, "Blackberry"]## 1 2 3 4 5
## 0.4133863 1.0258692 0.7334304 0.9735309 0.1709919AvEmergenceRate[, , "Cherry"][, "Blackberry"]/AvEmergenceRate[, , "Cherry"][, "Cherry"]## 1 2 3 4 5
## 1.4425369 0.8562518 1.0861981 0.8514873 1.4918117AvEmergenceRate[, , "Blackberry"][, "Cherry"]/AvEmergenceRate[, , "Cherry"][, "Cherry"]## 1 2 3 4 5
## 0.7219048 1.0522488 1.0774054 0.9037047 0.46513154.2 Analysis
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment,
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PERF_Rate)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[4,2])/(1/anova(m1)[4, 1]))## [1] 30.89092(pvalue <- 1 - pf(Fratio, 1, anova(m1)[4, 1])) ## [1] 0.0114893#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0,
data = data_PERF_Rate)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[5,2])/(1/anova(m2)[5, 1]))## [1] 59.30375(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[5, 1]) )## [1] 0.004550853## F test for SA
(Fratio_NonGen <- (anova(m2)[4,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 3.305405(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[6, 1]) )## [1] 0.1666402## Compute R2 for SA
## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(r2_SA_genet <- 1-(anova(m2)[5, 3]/((anova(m2)[3, 2]+anova(m2)[5, 2])/(anova(m2)[3, 1]+anova(m2)[5, 1]))))## [1] 0.9357984(r2_SA_nongenet <- 1-(anova(m2)[6, 3]/((anova(m2)[4, 2]+anova(m2)[6, 2])/(anova(m2)[4, 1]+anova(m2)[6, 1]))))## [1] 0.36562364.3 Analysis with eggs as covariable
#######################################################
## Should we consider the number of eggs? ###
#######################################################
# Original
m1 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0,
data = data_PERF_Rate)
## Correction for number of eggs
m2 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
log(Nb_eggs),
data = data_PERF_Rate)
## With egg score
m3 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScore,
data = data_PERF_Rate)
## Compare with 5 egg scores
m4 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScoreFive,
data = data_PERF_Rate)
## Compare with EggScoreSmall
m5 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
EggScoreSmall,
data = data_PERF_Rate)
MuMIn::model.sel(m1, m2, m3, m4, m5)## Model selection table
## (Int) hab_gen pop_gen SA IG0:SA Org_env:Tst_env IG0:Org_env:Tst_env
## m2 0.6870 + + + + + +
## m1 0.6144 + + + + + +
## m3 0.6155 + + + + + +
## m4 0.6145 + + + + + +
## m5 0.6159 + + + + + +
## log(Nb_egg) EgS ESF ESS family df logLik AICc delta weight
## m2 -0.06172 gaussian(identity) 63 -135.321 405.4 0.00 1
## m1 gaussian(identity) 62 -149.207 430.9 25.49 0
## m3 + gaussian(identity) 65 -146.871 433.0 27.68 0
## m4 + gaussian(identity) 66 -146.858 435.3 29.95 0
## m5 + gaussian(identity) 67 -146.342 436.6 31.22 0
## Models ranked by AICc(x)## Cl= The best model is when the number of eggs is considered as a continuous variable
### model m2 provides a better description of the data than model m1
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment +
log(Nb_eggs),
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PERF_Rate)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[5,2])/(1/anova(m1)[5, 1]))## [1] 27.56268(pvalue <- 1 - pf(Fratio, 1, anova(m1)[5, 1])) ## [1] 0.01345815## Compute R2 = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(rsqgen <- 1-anova(m1)[5, 3]/((anova(m1)[3, 2]+anova(m1)[4, 2])/(anova(m1)[3, 1]+anova(m1)[4, 1])))## [1] 0.9762833#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(asin(sqrt(Rate)) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
log(Nb_eggs),
data = data_PERF_Rate)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 37.67609(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[6, 1]) )## [1] 0.008696739## F test for SA
(Fratio_NonGen <- (anova(m2)[5,2]/anova(m2)[7,2])/(1/anova(m2)[7, 1]))## [1] 2.164733(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[7, 1]) )## [1] 0.2375861# Compute R2 = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
rsqgen <- 1-anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))
rsqng <- 1-anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))
## Compute R2 for SA
## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(r2_SA_genet <- 1-(anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))))## [1] 0.9016621(r2_SA_nongenet <- 1-(anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))))## [1] 0.2255167##Plot
(PLOT_rate_G0 <- plot_RTP_residuals(dataset = data_PERF_Rate, trait = "Rate", gen = "G0"))
(PLOT_rate_G2 <- plot_RTP_residuals(dataset = data_PERF_Rate, trait = "Rate", gen = "G2"))
(PLOT_GEN_rate_G0 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF_Rate,
trait = "Rate",
effect = "Non-genetic"))
(PLOT_GEN_rate_G2 <- plot_Genetic_Nongenetic_residuals(dataset = data_PERF_Rate,
trait = "Rate",
effect = "Genetic"))
4.4 Heterogeneity across populations
data_Rate_G2 <- data_PERF_Rate[data_PERF_Rate$Generation=="G2",]
data_Rate_G2 <- data_Rate_G2[complete.cases(data_Rate_G2$Rate), ]
m0 <- lm(Rate~Original_environment*Test_environment, data=data_Rate_G2,)
summary(m0)##
## Call:
## lm(formula = Rate ~ Original_environment * Test_environment,
## data = data_Rate_G2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.25791 -0.09879 -0.02409 0.07267 0.59923
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 0.25791 0.01305
## Original_environmentCherry -0.07232 0.02372
## Original_environmentStrawberry -0.03726 0.02372
## Test_environmentCherry -0.06560 0.01845
## Test_environmentStrawberry -0.12340 0.01841
## Original_environmentCherry:Test_environmentCherry 0.06017 0.03307
## Original_environmentStrawberry:Test_environmentCherry 0.04002 0.03342
## Original_environmentCherry:Test_environmentStrawberry 0.04816 0.03339
## Original_environmentStrawberry:Test_environmentStrawberry 0.06469 0.03352
## t value Pr(>|t|)
## (Intercept) 19.767 < 2e-16 ***
## Original_environmentCherry -3.049 0.002397 **
## Original_environmentStrawberry -1.571 0.116740
## Test_environmentCherry -3.555 0.000408 ***
## Test_environmentStrawberry -6.703 4.76e-11 ***
## Original_environmentCherry:Test_environmentCherry 1.820 0.069337 .
## Original_environmentStrawberry:Test_environmentCherry 1.198 0.231534
## Original_environmentCherry:Test_environmentStrawberry 1.442 0.149775
## Original_environmentStrawberry:Test_environmentStrawberry 1.930 0.054091 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1343 on 592 degrees of freedom
## Multiple R-squared: 0.1004, Adjusted R-squared: 0.08825
## F-statistic: 8.259 on 8 and 592 DF, p-value: 1.223e-10tapply(data_Rate_G2$Rate,
list(data_Rate_G2$Original_environment,
data_Rate_G2$Test_environment), mean)## Blackberry Cherry Strawberry
## Blackberry 0.2579090 0.1923140 0.1345134
## Cherry 0.1855892 0.1801664 0.1103531
## Strawberry 0.2206513 0.1950778 0.1619433tapply(data_Rate_G2$Rate,
list(data_Rate_G2$Original_environment,
data_Rate_G2$Test_environment), var)## Blackberry Cherry Strawberry
## Blackberry 0.03005193 0.013749525 0.01436498
## Cherry 0.02922031 0.019467766 0.01399160
## Strawberry 0.01555929 0.009464032 0.01139614range(data_Rate_G2$Nb_adults, na.rm = TRUE)## [1] 0 108## Check number of eggs and adults
tapply(data_Rate_G2$Nb_eggs,
list(data_Rate_G2$Original_environment,
data_Rate_G2$Test_environment), mean)## Blackberry Cherry Strawberry
## Blackberry 104.2170 121.8491 98.71963
## Cherry 131.7174 133.8400 103.51064
## Strawberry 119.1739 119.4894 111.56522tapply(data_Rate_G2$Nb_adults,
list(data_Rate_G2$Original_environment,
data_Rate_G2$Test_environment), mean)## Blackberry Cherry Strawberry
## Blackberry 26.82075 23.18868 13.15888
## Cherry 22.91304 23.32000 11.53191
## Strawberry 25.65217 22.36170 16.04348## Check for the presence of negative correlations
m1 <- lm(asin(sqrt(Rate)) ~ Population + Test_environment,
data=data_Rate_G2)
data_Rate_G2$resid <- residuals(m1)
meanbypopbytestenv <- as.data.frame(tapply(data_Rate_G2$resid,
list(data_Rate_G2$Population,
data_Rate_G2$Test_environment), mean))
## Cherry ~ Blackberry
plot(meanbypopbytestenv$Blackberry,
meanbypopbytestenv$Cherry,
col=as.numeric(data_Rate_G2$Original_environment[match(rownames(meanbypopbytestenv),
data_Rate_G2$Population)]),
xlab="Blackberry", ylab="Cherry", pch=16)
legend("topright", levels(data_Rate_G2$Original_environment),
col=as.numeric(as.factor(levels(data_Rate_G2$Original_environment))), pch=16)
## Strawberry ~ Cherry
plot(meanbypopbytestenv$Cherry, meanbypopbytestenv$Strawberry, col=as.numeric(data_Rate_G2$Original_environment[match(rownames(meanbypopbytestenv), data_Rate_G2$Population)]), xlab="Cherry", ylab="Strawberry", pch=16)
legend("bottomright", levels(data_Rate_G2$Original_environment), col=as.numeric(as.factor(levels(data_Rate_G2$Original_environment))), pch=16)
## Strawberry ~ Blackberry
plot(meanbypopbytestenv$Blackberry, meanbypopbytestenv$Strawberry, col=as.numeric(data_Rate_G2$Original_environment[match(rownames(meanbypopbytestenv), data_Rate_G2$Population)]), xlab="Blackberry", ylab="Strawberry", pch=16)
legend("topright", levels(data_Rate_G2$Original_environment), col=as.numeric(as.factor(levels(data_Rate_G2$Original_environment))), pch=16)
###PLOT
(PAIR_BLACK_CHERRY_G2_RATE <- plot_PairwisePOP_residuals(dataset = data_PERF_Rate, trait = "Rate", gen = "G2",
fruit1 = "Cherry", fruit2 = "Blackberry"))
(PAIR_CHERRY_STRAW_G2_RATE <- plot_PairwisePOP_residuals(dataset = data_PERF_Rate, trait = "Rate", gen = "G2",
fruit1 = "Strawberry", fruit2 = "Cherry"))
(PAIR_STRW_BLACK_G2_RATE <- plot_PairwisePOP_residuals(dataset = data_PERF_Rate, trait = "Rate", gen = "G2",
fruit1 = "Blackberry", fruit2 = "Strawberry"))
(PAIR_BLACK_CHERRY_RATE <- plot_PairwisePOP_residuals(dataset = data_PERF_Rate,
trait = "Rate",
gen = "Both",
fruit1 = "Cherry",
fruit2 = "Blackberry"))
(PAIR_CHERRY_STRAW_RATE <- plot_PairwisePOP_residuals(dataset = data_PERF_Rate,
trait = "Rate",
gen = "Both",
fruit1 = "Strawberry",
fruit2 = "Cherry"))
(PAIR_STRW_BLACK_RATE <- plot_PairwisePOP_residuals(dataset = data_PERF_Rate,
trait = "Rate",
gen = "Both",
fruit1 = "Blackberry",
fruit2 = "Strawberry"))
5 OVIOSITION PREFERENCE
5.1 Twelve fruits
5.1.1 Exploration data
tapply(data_PREF$Nb_eggs, list(data_PREF$Original_environment,
data_PREF$Test_environment,
data_PREF$Generation), mean, na.rm = TRUE)## , , G0
##
## Apricot Blackberry Blackcurrant Cherry Cranberry Fig
## Blackberry 0.2758621 0.7931034 0.1379310 0.4137931 0.2068966 0.2586207
## Cherry 0.3300000 0.5000000 1.2600000 2.4800000 0.3800000 0.7400000
## Strawberry 0.5714286 1.1904762 0.6666667 1.6666667 0.5714286 0.4285714
## Grape Kiwi Raspberry Rosehips Strawberry Tomato
## Blackberry 0.2068966 0.0862069 0.4655172 0.3793103 0.5862069 0.2241379
## Cherry 0.9700000 1.0500000 1.5800000 1.2400000 0.6700000 0.6969697
## Strawberry 0.4761905 0.1428571 0.6666667 0.3809524 0.8571429 0.0952381
##
## , , G2
##
## Apricot Blackberry Blackcurrant Cherry Cranberry Fig
## Blackberry 6.826531 24.62245 18.61224 23.41837 8.428571 12.73469
## Cherry 10.423077 16.09615 19.09615 30.38462 7.480769 13.46154
## Strawberry 15.450000 21.92500 22.12500 24.92500 7.200000 13.02500
## Grape Kiwi Raspberry Rosehips Strawberry Tomato
## Blackberry 24.55102 16.57143 16.59184 11.23469 6.938776 12.53061
## Cherry 11.67308 11.90385 12.78846 11.46154 10.673077 10.59615
## Strawberry 12.55000 14.87500 19.45000 16.20000 12.775000 14.90000ggplot2::ggplot(data = data_PREF,
aes(x = Test_environment, y = Nb_eggs, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PREF,
aes(x = Nb_eggs, fill = Original_environment)) +
facet_wrap( ~ Generation) +
geom_histogram(position="identity", alpha=0.5) +
theme_LO_sober
5.1.2 Analysis
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment,
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PREF)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[4,2])/(1/anova(m1)[4, 1]))## [1] 10.1742(pvalue <- 1 - pf(Fratio, 1, anova(m1)[4, 1])) ## [1] 0.004407544#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
BoxID,
data = data_PREF)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 9.833047(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[6, 1]) )## [1] 0.004993505## F test for SA
(Fratio_NonGen <- (anova(m2)[5,2]/anova(m2)[7,2])/(1/anova(m2)[7, 1]))## [1] 2.500166(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[7, 1]) )## [1] 0.1287796## Compute R2 for SA
## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
(r2_SA_genet <- 1-(anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))))## [1] 0.2864799(r2_SA_nongenet <- 1-(anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))))## [1] 0.0638364#Local adaptation pattern:
lm_val = lm(log(Nb_eggs+1) ~ Test_environment + Population + SA +
Test_environment:Original_environment + BoxID,
data = data_PREF[data_PREF$Generation=="G0",])
(Fratio = anova(lm_val)[3,3]/anova(lm_val)[5,3])## [1] 4.476081(pvalue = 1 - pf(Fratio,anova(lm_val)[3,1],anova(lm_val)[5,1]))## [1] 0.0464917(df1 = anova(lm_val)[3,1])## [1] 1(df2 = anova(lm_val)[5,1])## [1] 21lm_val = lm(log(Nb_eggs+1) ~ Test_environment + Population + SA +
Test_environment:Original_environment + BoxID,
data = data_PREF[data_PREF$Generation=="G2",])
(Fratio = anova(lm_val)[3,3]/anova(lm_val)[5,3])## [1] 6.319972(pvalue = 1 - pf(Fratio,anova(lm_val)[3,1],anova(lm_val)[5,1]))## [1] 0.02016054(df1 = anova(lm_val)[3,1])## [1] 1(df2 = anova(lm_val)[5,1])## [1] 215.2 Three fruits
5.2.1 Exploration data
tapply(data_PREF_three$Nb_eggs, list(data_PREF_three$Original_environment,
data_PREF_three$Test_environment,
data_PREF_three$Generation), mean, na.rm = TRUE)## , , G0
##
## Blackberry Cherry Strawberry
## Blackberry 0.7931034 0.4137931 0.5862069
## Cherry 0.5000000 2.4800000 0.6700000
## Strawberry 1.1904762 1.6666667 0.8571429
##
## , , G2
##
## Blackberry Cherry Strawberry
## Blackberry 24.62245 23.41837 6.938776
## Cherry 16.09615 30.38462 10.673077
## Strawberry 21.92500 24.92500 12.775000ggplot2::ggplot(data = data_PREF_three,
aes(x = Test_environment, y = Nb_eggs, color = Test_environment)) +
facet_wrap( ~ Population) +
geom_point() +
geom_boxplot() +
theme_LO_sober
ggplot2::ggplot(data = data_PREF_three,
aes(x = Nb_eggs, fill = Original_environment)) +
facet_wrap( ~ Generation) +
geom_histogram(position="identity", alpha=0.5) +
theme_LO_sober
5.2.2 Analysis
#######################################################
## Analysis of genetic effects lm ###
#######################################################
m1 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA +
Original_environment:Test_environment +
BoxID,
contrasts = list(Original_environment = "contr.sum",
Test_environment = "contr.sum"),
data = data_PREF_three)
## F test for SA
(Fratio <- (anova(m1)[3,2]/anova(m1)[5,2])/(1/anova(m1)[5, 1]))## [1] 18.38385(pvalue <- 1 - pf(Fratio, 1, anova(m1)[5, 1])) ## [1] 0.02331823#######################################################
## Analysis of non-genetic effects lm ###
#######################################################
m2 <- aov(log(Nb_eggs+1) ~ pop_gen + hab_gen + SA:IndicG0 + SA +
Original_environment:Test_environment +
Original_environment:Test_environment:IndicG0 +
BoxID,
data = data_PREF_three)
## F test for SA
(Fratio_Gen <- (anova(m2)[3,2]/anova(m2)[6,2])/(1/anova(m2)[6, 1]))## [1] 12.77418(pvalue_Gen <- 1 - pf(Fratio_Gen, 1, anova(m2)[6, 1]) )## [1] 0.0374429## F test for SA
(Fratio_NonGen <- (anova(m2)[5,2]/anova(m2)[7,2])/(1/anova(m2)[7, 1]))## [1] 3.536493(pvalue_NonGen <- 1 - pf(Fratio_NonGen, 1, anova(m2)[7, 1]) )## [1] 0.1566089## Compute R2 for SA
# ## = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
# (r2_SA_genet <- 1-(anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))))
#
# (r2_SA_nongenet <- 1-(anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))))
## Compute R2 = MS Interaction model without SA - MS Interaction model with SA / MS Interaction model without SA
rsqgen <- 1-anova(m2)[6, 3]/((anova(m2)[3, 2]+anova(m2)[6, 2])/(anova(m2)[3, 1]+anova(m2)[6, 1]))
rsqng <- 1-anova(m2)[7, 3]/((anova(m2)[5, 2]+anova(m2)[7, 2])/(anova(m2)[5, 1]+anova(m2)[7, 1]))
rsqgen## [1] 0.746421 rsqng## [1] 0.38805115.2.3 Plot
(PLOT_pref_G0 <- plot_RTP_residuals(dataset = data_PREF_three,
trait = "Nb_eggs", gen = "G0"))
(PLOT_pref_G2 <- plot_RTP_residuals(dataset = data_PREF_three,
trait = "Nb_eggs", gen = "G2"))
(PLOT_GEN_pref_G0 <- plot_Genetic_Nongenetic_residuals(dataset = data_PREF_three,
trait = "Nb_eggs", effect = "Non-genetic"))
(PLOT_GEN_pref_G2 <- plot_Genetic_Nongenetic_residuals(dataset = data_PREF_three,
trait = "Nb_eggs", effect = "Genetic"))
# ###PLOT
(PAIR_BLACK_CHERRY_G2_PREF <- plot_PairwisePOP_residuals(dataset = data_PREF_three,
trait = "Nb_eggs", gen = "G2",
fruit1 = "Cherry", fruit2 = "Blackberry"))
#
# (PAIR_CHERRY_STRAW_G2_PREF <- plot_PairwisePOP_residuals(dataset = data_PREF_three,
# trait = "Nb_eggs", gen = "G2",
# fruit1 = "Strawberry", fruit2 = "Cherry"))
#
# (PAIR_STRW_BLACK_G2_PREF <- plot_PairwisePOP_residuals(dataset = data_PREF_three,
# trait = "Nb_eggs", gen = "G2",
# fruit1 = "Blackberry", fruit2 = "Strawberry"))
(PAIR_BLACK_CHERRY_PREF <- plot_PairwisePOP_residuals(dataset = data_PREF_three,
trait = "Nb_eggs",
gen = "Both",
fruit1 = "Cherry",
fruit2 = "Blackberry"))
(PAIR_CHERRY_STRAW_PREF <- plot_PairwisePOP_residuals(dataset = data_PREF_three,
trait = "Nb_eggs",
gen = "Both",
fruit1 = "Strawberry",
fruit2 = "Cherry"))
(PAIR_STRW_BLACK_PREF <- plot_PairwisePOP_residuals(dataset = data_PREF_three,
trait = "Nb_eggs",
gen = "Both",
fruit1 = "Blackberry",
fruit2 = "Strawberry"))
6 PLOT LOCAL ADAPTATION
legend <- lemon::g_legend(PLOT_pref_G0)
#
# ############## FIRST GENERATION
# LOCAL_ADAPTATION_PLOT_FRST <- cowplot::ggdraw() +
# cowplot::draw_plot(PLOT_eggs_G0+theme(legend.position = "none",
# plot.title = element_blank()),
# x =0, y = 0.5, width = 0.36, height = 0.46) +
# cowplot::draw_plot(PLOT_adult_G0+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.4, y = 0.5, width =0.36, height = 0.46) +
# cowplot::draw_plot(PLOT_rate_G0+theme(legend.position = "none",
# plot.title = element_blank()),
# x =0, y = 0, width = 0.36, height = 0.46) +
# cowplot::draw_plot(PLOT_pref_G0+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.4, y = 0, width = 0.36, height = 0.46) +
# cowplot::draw_plot(legend, x = 0.85, y = 0.5, width = 0.0001, height = 0.0001) +
# cowplot::draw_plot_label(c("A", "B", "C", "D"),
# c(0.01, 0.4,0.01, 0.4),
# c(1, 1,0.5,0.5), size = 19)
# # + cowplot::draw_plot_label("First generation", x = 0.2, y = 1 , size = 14)
# LOCAL_ADAPTATION_PLOT_FRST
#
#
# # cowplot::save_plot(file =here::here("figures","Reciprocal_experiment_FirstGeneration.pdf"),
# # LOCAL_ADAPTATION_PLOT_FRST, base_height = 20/cm(1),
# # base_width = 30/cm(1), dpi = 1200)
#
#
# ############## THIRD GENERATION
# LOCAL_ADAPTATION_PLOT_THIRD <- cowplot::ggdraw() +
# cowplot::draw_plot(PLOT_eggs_G0+theme(legend.position = "none",
# plot.title = element_blank()),
# x =0, y = 0.5, width = 0.36, height = 0.46) +
# cowplot::draw_plot(PLOT_rate_G0+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.4, y = 0.5, width =0.36, height = 0.46) +
# cowplot::draw_plot(PLOT_rate_G0+theme(legend.position = "none",
# plot.title = element_blank()),
# x =0, y = 0, width = 0.36, height = 0.46) +
# cowplot::draw_plot(PLOT_pref_G0+theme(legend.position = "none",
# plot.title = element_blank()),
# x =0, y = 0.5, width = 0.36, height = 0.46) +
# cowplot::draw_plot(PLOT_adult_G2+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.4, y = 0.5, width =0.36, height = 0.46) +
# cowplot::draw_plot(PLOT_rate_G2+theme(legend.position = "none",
# plot.title = element_blank()),
# x =0, y = 0, width = 0.36, height = 0.46) +
# cowplot::draw_plot(PLOT_pref_G2+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.4, y = 0, width = 0.36, height = 0.46) +
# cowplot::draw_plot(legend, x = 0.85, y = 0.5, width = 0.0001, height = 0.0001) +
# cowplot::draw_plot_label(c("A", "B", "C", "D"),
# c(0.01, 0.4,0.01, 0.4),
# c(1, 1,0.5,0.5), size = 19)
# LOCAL_ADAPTATION_PLOT_THIRD
# #
#
# cowplot::save_plot(file = here::here("figures","Reciprocal_experiment_ThirdGeneration.pdf"),
# LOCAL_ADAPTATION_PLOT_THIRD, base_height = 20/cm(1),
# base_width = 30/cm(1), dpi = 1200)
#
## ALL GENERATIONS
LOCAL_ADAPTATION_PLOT <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_pref_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.01, y = 0.5, width = 0.25, height = 0.45) +
cowplot::draw_plot(PLOT_eggs_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.31, y = 0.5, width = 0.25, height = 0.45) +
cowplot::draw_plot(PLOT_rate_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.61, y = 0.5, width = 0.25, height = 0.45) +
cowplot::draw_plot(legend, x = 0.93, y = 0.5, width = 0.0001, height = 0.0001) +
cowplot::draw_plot(PLOT_pref_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.01, y = 0, width = 0.25, height = 0.45) +
cowplot::draw_plot(PLOT_eggs_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.31, y = 0, width = 0.25, height = 0.45) +
cowplot::draw_plot(PLOT_rate_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.61, y = 0, width = 0.25, height = 0.45) +
cowplot::draw_plot_label(c("Generation: G0","Generation: G0","Generation: G1", "A", "B", "C", " ",
"Generation: G2","Generation: G2","Generation: G3", "D", "E", "F", " "),
x = c(0.1,0.4,0.7, 0.01, 0.30, 0.61, 0.92, 0.10,0.4,0.7, 0.01, 0.3, 0.61, 0.92),
y = c(1,1,1, 0.98, 0.98, 0.98, 0.98, 0.5, 0.5, 0.5, 0.48, 0.48, 0.48, 0.48),
hjust = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0),
vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
size = 16)
LOCAL_ADAPTATION_PLOT
# cowplot::save_plot(file =here::here("figures", "LOCAL_ADAPTATION_G0_G2.pdf"),
# LOCAL_ADAPTATION_PLOT,
# base_height = 18/cm(1), base_width = 34/cm(1), dpi = 610)
#
#A tej:
# cowplot::draw_plot_label(c("Generation: G0", "A", "B", "C", " ",
# "Generation: G2", "D", "E", "F", " "),
# x = c(0.4, 0.01, 0.30, 0.61, 0.92, 0.4, 0.01, 0.3, 0.61, 0.92),
# y = c(1, 0.98, 0.98, 0.98, 0.98, 0.5, 0.48, 0.48, 0.48, 0.48),
# hjust = c(0,0,0,0,0,0,0,0,0,0),
# vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
# size = 16) 7 PLOT GENETIC vs PLASTIC
legend <- lemon::g_legend(PLOT_pref_G0)
Genetic_NonGenetic_PLOT <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_GEN_pref_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.01, y = 0.5, width = 0.25, height = 0.45) +
cowplot::draw_plot(PLOT_GEN_eggs_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.31, y = 0.5, width = 0.25, height = 0.45) +
cowplot::draw_plot(PLOT_GEN_rate_G2 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.61, y = 0.5, width = 0.25, height = 0.45) +
cowplot::draw_plot(legend, x = 0.93, y = 0.5, width = 0.0001, height = 0.0001) +
cowplot::draw_plot(PLOT_GEN_pref_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.01, y = 0, width = 0.25, height = 0.45) +
cowplot::draw_plot(PLOT_GEN_eggs_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.31, y = 0, width = 0.25, height = 0.45) +
cowplot::draw_plot(PLOT_GEN_rate_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.61, y = 0, width = 0.25, height = 0.45) +
cowplot::draw_plot_label(c("Genetic effects", "A", "B", "C", " ",
"Plastic effects", "D", "E", "F", " "),
x = c(0.4, 0.01, 0.30, 0.61, 0.92, 0.4, 0.01, 0.3, 0.61, 0.92),
y = c(1, 0.98, 0.98, 0.98, 0.98, 0.5, 0.48, 0.48, 0.48, 0.48),
hjust = c(0,0,0,0,0,0,0,0,0,0),
vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
size = 16)
Genetic_NonGenetic_PLOT
#
# cowplot::save_plot(file =here::here("figures", "GENETIC_NONGENETIC_First_Third.pdf"),
# Genetic_NonGenetic_PLOT,
# base_height = 18/cm(1), base_width = 34/cm(1), dpi = 610)
# 8 PLOT HETEROGENEITY
legend <- lemon::g_legend(PAIR_BLACK_CHERRY_G2_PREF)
# ############## THIRD GENERATION
# HETEROGENEITY_PLOT_THIRD <- cowplot::ggdraw() +
# cowplot::draw_plot(PAIR_BLACK_CHERRY_G2_EGGS+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0, y = 0.75, width = 0.25, height = 0.25) +
# cowplot::draw_plot(PAIR_CHERRY_STRAW_G2_EGGS+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.30, y = 0.75, width = 0.25, height = 0.25) +
# cowplot::draw_plot(PAIR_STRW_BLACK_G2_EGGS+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.60, y = 0.75, width = 0.25, height = 0.25) +
# #cowplot::draw_plot(legend, x = 0.85, y = 0.8, width = 0.0001, height = 0.0001) +
# cowplot::draw_plot(PAIR_BLACK_CHERRY_G2_RATE+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0, y = 0.5, width = 0.25, height = 0.25) +
# cowplot::draw_plot(PAIR_CHERRY_STRAW_G2_RATE+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.30, y = 0.5, width = 0.25, height = 0.25) +
# cowplot::draw_plot(PAIR_STRW_BLACK_G2_RATE+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.60, y = 0.5, width = 0.25, height = 0.25) +
# cowplot::draw_plot(PAIR_BLACK_CHERRY_G2_ADULTS+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0, y = 0.25, width = 0.25, height = 0.25) +
# cowplot::draw_plot(PAIR_CHERRY_STRAW_G2_ADULTS+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.30, y = 0.25, width = 0.25, height = 0.25) +
# cowplot::draw_plot(PAIR_STRW_BLACK_G2_ADULTS+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.60, y = 0.25, width = 0.25, height = 0.25) +
# cowplot::draw_plot(PAIR_BLACK_CHERRY_G2_PREF+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0, y = 0, width = 0.25, height = 0.25) +
# cowplot::draw_plot(PAIR_CHERRY_STRAW_G2_PREF+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.30, y = 0, width = 0.25, height = 0.25) +
# cowplot::draw_plot(PAIR_STRW_BLACK_G2_PREF+theme(legend.position = "none",
# plot.title = element_blank()),
# x = 0.60, y = 0, width = 0.25, height = 0.25) +
# cowplot::draw_plot(legend, x = 0.93, y = 0.5, width = 0.0001, height = 0.0001)
# HETEROGENEITY_PLOT_THIRD
# cowplot::save_plot(file =here::here("figures","Heterogeneity_Across_Pop_ThirdGeneration.pdf"),
# HETEROGENEITY_PLOT_THIRD, base_height = 35/cm(1),
# base_width = 35/cm(1), dpi = 1200)
#
legend <- lemon::g_legend(PAIR_CHERRY_STRAW_PREF)
############## ALL GENERATION
HETEROGENEITY_PLOT <- cowplot::ggdraw() +
cowplot::draw_plot(PAIR_BLACK_CHERRY_PREF+theme(legend.position = "none",
plot.title = element_blank()),
x = 0, y = 0.66, width = 0.28, height = 0.28) +
cowplot::draw_plot(PAIR_CHERRY_STRAW_PREF+theme(legend.position = "none",
plot.title = element_blank()),
x = 0.30, y = 0.66, width = 0.28, height = 0.28) +
cowplot::draw_plot(PAIR_STRW_BLACK_PREF+theme(legend.position = "none",
plot.title = element_blank()),
x = 0.60, y = 0.66, width = 0.28, height = 0.28) +
#cowplot::draw_plot(legend, x = 0.85, y = 0.8, width = 0.0001, height = 0.0001) +
cowplot::draw_plot(PAIR_BLACK_CHERRY_EGGS+theme(legend.position = "none",
plot.title = element_blank()),
x = 0, y = 0.33, width = 0.28, height = 0.28) +
cowplot::draw_plot(PAIR_CHERRY_STRAW_EGGS+theme(legend.position = "none",
plot.title = element_blank()),
x = 0.30, y = 0.33, width = 0.28, height = 0.28) +
cowplot::draw_plot(PAIR_STRW_BLACK_EGGS+theme(legend.position = "none",
plot.title = element_blank()),
x = 0.60, y = 0.33, width = 0.28, height = 0.28) +
cowplot::draw_plot(PAIR_BLACK_CHERRY_RATE+theme(legend.position = "none",
plot.title = element_blank()),
x = 0, y = 0, width = 0.28, height = 0.28) +
cowplot::draw_plot(PAIR_CHERRY_STRAW_RATE+theme(legend.position = "none",
plot.title = element_blank()),
x = 0.30, y = 0, width = 0.28, height = 0.28) +
cowplot::draw_plot(PAIR_STRW_BLACK_RATE+theme(legend.position = "none",
plot.title = element_blank()),
x = 0.60, y = 0, width = 0.28, height = 0.28) +
cowplot::draw_plot(legend, x = 0.93, y = 0.5, width = 0.0001, height = 0.0001) +
cowplot::draw_plot_label(c("Oviposition preference",
"Oviposition stimulation",
"Egg-to-adult survival"),
x = c(0.48,0.48,0.48),
y = c(0.96, 0.63, 0.29),
hjust = c(0.5,0.5,0.5),
vjust = c(0.5,0.5,0.5),
size = 16)
# HETEROGENEITY_PLOT
#
#
cowplot::save_plot(file =here::here("figures","Heterogeneity_Across_Pop.pdf"),
HETEROGENEITY_PLOT, base_height = 30/cm(1),
base_width = 30/cm(1), dpi = 1200)9 RELATION BETWEEN TRAITS
PLOT_Blackberry <- plot_RelationTraits_residuals(gen = "G2",
fruit = "Blackberry",
trait2 = "Preference")
PLOT_Cherry <- plot_RelationTraits_residuals(gen = "G2",
fruit = "Cherry",
trait2 = "Preference")
PLOT_Strawberry <- plot_RelationTraits_residuals(gen = "G2",
fruit = "Strawberry",
trait2 = "Preference")
PLOT_Blackberry_G0 <- plot_RelationTraits_residuals(gen = "G0",
fruit = "Blackberry",
trait2 = "Preference")
PLOT_Cherry_G0 <- plot_RelationTraits_residuals(gen = "G0",
fruit = "Cherry",
trait2 = "Preference")
PLOT_Strawberry_G0 <- plot_RelationTraits_residuals(gen = "G0",
fruit = "Strawberry",
trait2 = "Preference")
legend <- lemon::g_legend(PLOT_Strawberry_G0)
## ALL GENERATIONS
RELATION_TRAITS_G0G2 <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_Blackberry_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.01, y = 0.5, width = 0.24, height = 0.43) +
cowplot::draw_plot(PLOT_Cherry_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.31, y = 0.5, width = 0.24, height = 0.43) +
cowplot::draw_plot(PLOT_Strawberry_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.61, y = 0.5, width = 0.24, height = 0.43) +
cowplot::draw_plot(legend, x = 0.93, y = 0.5, width = 0.0001, height = 0.0001) +
cowplot::draw_plot(PLOT_Blackberry + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.01, y = 0, width = 0.24, height = 0.43) +
cowplot::draw_plot(PLOT_Cherry + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.31, y = 0, width = 0.24, height = 0.43) +
cowplot::draw_plot(PLOT_Strawberry + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.61, y = 0, width = 0.24, height = 0.43) +
cowplot::draw_plot_label(c("Generation: G0/G1", "A", "B", "C", " ",
"Generation: G2/G3", "D", "E", "F", " "),
x = c(0.4, 0.01, 0.30, 0.61, 0.92, 0.4, 0.01, 0.3, 0.61, 0.92),
y = c(1, 0.98, 0.98, 0.98, 0.98, 0.5, 0.48, 0.48, 0.48, 0.48),
hjust = c(0,0,0,0,0,0,0,0,0,0),
vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
size = 16)
RELATION_TRAITS_G0G2
#
# cowplot::save_plot(file =here::here("figures", "RELATION_TRAITS_G0G2.pdf"),
# RELATION_TRAITS_G0G2,
# base_height = 18/cm(1), base_width = 34/cm(1), dpi = 610)
#
#
##### With stimulation
PLOT_Blackberry_Stim <- plot_RelationTraits_residuals(gen = "G2",
fruit = "Blackberry",
trait2 = "Stimulation")
PLOT_Cherry_Stim <- plot_RelationTraits_residuals(gen = "G2",
fruit = "Cherry",
trait2 = "Stimulation")
PLOT_Strawberry_Stim <- plot_RelationTraits_residuals(gen = "G2",
fruit = "Strawberry",
trait2 = "Stimulation")
PLOT_Blackberry_Stim_G0 <- plot_RelationTraits_residuals(gen = "G0",
fruit = "Blackberry",
trait2 = "Stimulation")
PLOT_Cherry_Stim_G0 <- plot_RelationTraits_residuals(gen = "G0",
fruit = "Cherry",
trait2 = "Stimulation")
PLOT_Strawberry_Stim_G0 <- plot_RelationTraits_residuals(gen = "G0",
fruit = "Strawberry",
trait2 = "Stimulation")
## ALL GENERATIONS
RELATION_TRAITS_Stimulation_G0G2 <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_Blackberry_Stim_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.01, y = 0.5, width = 0.24, height = 0.43) +
cowplot::draw_plot(PLOT_Cherry_Stim_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.31, y = 0.5, width = 0.24, height = 0.43) +
cowplot::draw_plot(PLOT_Strawberry_Stim_G0 + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.61, y = 0.5, width = 0.24, height = 0.43) +
cowplot::draw_plot(legend, x = 0.93, y = 0.5, width = 0.0001, height = 0.0001) +
cowplot::draw_plot(PLOT_Blackberry_Stim + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.01, y = 0, width = 0.24, height = 0.43) +
cowplot::draw_plot(PLOT_Cherry_Stim + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.31, y = 0, width = 0.24, height = 0.43) +
cowplot::draw_plot(PLOT_Strawberry_Stim + theme(legend.position = "none",
plot.title = element_blank()),
x = 0.61, y = 0, width = 0.24, height = 0.43) +
cowplot::draw_plot_label(c("Generation: G0/G1", "A", "B", "C", " ",
"Generation: G2/G3", "D", "E", "F", " "),
x = c(0.4, 0.01, 0.30, 0.61, 0.92, 0.4, 0.01, 0.3, 0.61, 0.92),
y = c(1, 0.98, 0.98, 0.98, 0.98, 0.5, 0.48, 0.48, 0.48, 0.48),
hjust = c(0,0,0,0,0,0,0,0,0,0),
vjust = c(1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5, 1.5),
size = 16)
RELATION_TRAITS_Stimulation_G0G2
# cowplot::save_plot(file =here::here("figures", "RELATION_TRAITS_Stimulation_G0G2.pdf"),
# RELATION_TRAITS_Stimulation_G0G2,
# base_height = 18/cm(1), base_width = 34/cm(1), dpi = 610)